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23 results on '"Geffner H."'

1. PDDL 2.1: Representation vs. Computation

Author

Geffner, H. A.

Subjects
Computer Science - Artificial Intelligence
Abstract

I comment on the PDDL 2.1 language and its use in the planning competition, focusing on the choices made for accommodating time and concurrency. I also discuss some methodological issues that have to do with the move toward more expressive planning languages and the balance needed in planning research between semantics and computation.

Published
2011
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2. mGPT: A Probabilistic Planner Based on Heuristic Search

Author

Bonet, B. and Geffner, H.

Subjects
Computer Science - Artificial Intelligence
Abstract

We describe the version of the GPT planner used in the probabilistic track of the 4th International Planning Competition (IPC-4). This version, called mGPT, solves Markov Decision Processes specified in the PPDDL language by extracting and using different classes of lower bounds along with various heuristic-search algorithms. The lower bounds are extracted from deterministic relaxations where the alternative probabilistic effects of an action are mapped into different, independent, deterministic actions. The heuristic-search algorithms use these lower bounds for focusing the updates and delivering a consistent value function over all states reachable from the initial state and the greedy policy.

Published
2011
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3. Inference and Decomposition in Planning Using Causal Consistent Chains

Author

NIR LIPOVETZKY and Geffner, H.

Abstract

Current state-of-the-art planners solve problems, easy and hard alike, by search, expanding hundreds or thousands of nodes. Yet, given the ability of people to solve easy problems and to explain their solutions, it seems that an essential inferential component may be missing. The reasons expressed by people for selecting actions appear to be related to causal chains: sequences of causal links ai → pi + 1, i = 0, ..., n – 1, such that a0 is applicable in the current state, pi is a precondition of action ai, and pn is a goal. Some of these causal chains or paths appear to be good, some bad, others appear to be impossible. In this work, we focus on such paths and develop three techniques for performing inference over them from which a path-based planner is obtained. We define the conditions under which a path is consistent, provide an heuristic estimate of the cost of achieving the goal along a consistent path, and introduce a planning algorithm that uses paths as decomposition backbones. The resulting planner, called C3, is not complete and does not perform as well as recent planners that carry extensive but extremely efficient searches such as LAMA, but is competitive with FF and in particular, with FF running in EHC mode which yields very focused but incomplete searches, and thus provides, a more apt comparison. Moreover, many domains are solved backtrack-free, with no search at all, suggesting that planning with paths may be a meaningful idea both cognitively and computationally.

Published
2009
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19. Reports on the Twenty-First National Conference on Artificial Intelligence (AAAI-06) workshop program

Author

Achtner, W., Aimeur, E., Anand, S. S., Appelt, D., Ashish, N., Barnes, T., Beck, J. E., Michael Beetz, Dias, M. B., Doshi, P., Drummond, C., Elazmeh, W., Felner, A., Freitag, D., Geffner, H., Geib, C. W., Goodwin, R., Holte, R. C., Hutter, F., Isaac, F., Japkowicz, N., Kaminka, G. A., Koenig, S., Lagoudakis, M. G., Leake, D., Lewis, L., Liu, H., Metzler, T., Mihalcea, R., Mobasher, B., Poupart, P., Pynadath, D. V., Rajan, K., Roth-Berghofer, T., Ruml, W., Rusu, R. B., Schulz, S., Schwarz, S., Seneff, S., Sheth, A., Sun, R., Thielscher, M., Upal, A., Williams, J., Young, S., and Zelenko, D.

20. AIPS-98 planning competition

Author

Long, D., Kautz, H., Selman, B., Bonet, B., Geffner, H., Koehler, J., Brenner, M., Hoffmann, J., Rittinger, F., Anderson, C. R., Weld, D. S., Smith, D. E., and maria fox

23. Computational models of planning.

Author

Geffner H

Abstract

The selection of the action to do next is one of the central problems faced by autonomous agents. Natural and artificial systems address this problem in various ways: action responses can be hardwired, they can be learned, or they can be computed from a model of the situation, the actions, and the goals. Planning is the model-based approach to action selection and a fundamental ingredient of intelligent behavior in both humans and machines. Planning, however, is computationally hard as the consideration of all possible courses of action is not computationally feasible. The problem has been addressed by research in Artificial Intelligence that in recent years has uncovered simple but powerful computational principles that make planning feasible. The principles take the form of domain-independent methods for computing heuristics or appraisals that enable the effective generation of goal-directed behavior even over huge spaces. In this paper, we look at several planning models, at methods that have been shown to scale up to large problems, and at what these methods may suggest about the human mind. WIREs Cogn Sci 2013, 4:341-356. doi: 10.1002/wcs.1233 The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website., (Copyright © 2013 John Wiley & Sons, Ltd.)

Published
2013
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